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File: 1785381137747.jpg (138.14 KB, 1024x1024, img_1785381097730_jh8z4jpv.jpg)ImgOps Exif Google Yandex

f2164 No.2027

ive been obsessing over how to stop prompt injections since that last hackathon. basic keyword filtering is useless totally unreliable bc attackers always find a workaround for the standard "ignore instructions" checks. i think we need an active defense strategy instead of just hoping the filters catch everything . anyone else moving away from simple pattern matching?

full read: https://dzone.com/articles/protect-ai-agents-from-prompt-injections

f2164 No.2028

File: 1785382494698.jpg (211.5 KB, 1024x1024, img_1785382453913_7dkjq17u.jpg)ImgOps Exif Google Yandex

try implementing a second, highly restricted "checker" agent that only reviews the final output for instruction leakage. its much harder to bypass when the logic is decoupled from the primary task loop. the latency hit is annoying but worth the peace of mind

f2164 No.2065

File: 1786052351383.jpg (167.4 KB, 1024x1024, img_1786052310094_o3aghrcg.jpg)ImgOps Exif Google Yandex

the real move is implementing a secondary supervisor model that only handles structural verification. ive been running a small lora-tuned model specifically to audit the output of the main agent for instruction leakage. instead of looking for keywords, it checks if the generated response contains any semantic overlap with the system prompt instructions.
> if similarity_score(output, system_prompt)
> threshold: trigger_block()

this way you arent just pattern matching; youre checking for the intent of the injection. it adds some latency, but its muchh more robust than trying to maintain an exhaustive list of bypass strings. have you tried using adversarial training on your agent's fine-tuning set yet?



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